Methods, systems, and computer program products using artificial intelligence for coordinated identification of patients for a clinical trial based on social determinants of health information

ABSTRACT

A method includes obtaining patient medical record information from a plurality of providers until a demographic profile of patients associated with the patient medical record information meets a desired demographic profile; querying the patient medical record information using selection criteria to identify a first subset of patients having first characteristics that match first screening requirements for a clinical trial; identifying, using an artificial intelligence engine, ones of the first subset of patients whose medical record information includes second characteristics that match second screening requirements for the clinical trial as clinical trial patient candidates; and communicating identities of the clinical trial patient candidates to ones of the plurality of providers that provide healthcare services to the clinical trial patient candidates, respectively. The plurality of providers are associated with a plurality of different organizational managing entities, respectively.

RELATED APPLICATION

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 17/218,487, filed Mar. 31, 2021, the disclosure ofwhich is hereby incorporated herein by reference in its entirety.

FIELD

The present inventive concepts relate generally to health care systemsand services and, more particularly, identification and recruitment ofpatients for clinical trials.

BACKGROUND

Many clinical trials are behind schedule due to enrollment delays andlack of awareness of patients about the opportunity to accessalternative treatment options through clinical research. The delays inconducting clinical trials may cause delays in the testing and approvalof new therapies for those who need them. Statistics have shown thatless than 1% of patients participate in clinical trials. Due to thesmall number of participants, clinical trials may have significantenrollment challenges to ensure there is an appropriate diversity ofparticipants. Oftentimes a large majority of potentially eligiblepatients are seen in a community care center where research is nottaking place and patients' medical records are not screened. In providerfacilities where clinical research is conducted, as little as 5% ofpatient records may be screened. Lack of awareness of ongoing trials bytreating physicians caused by the lack of a defined role for physiciansin the trial recruitment process further limits patient enrollment intoclinical trials. Further complicating the process for populating aclinical trial, the demographics of the participants may not reflectthat of the general population or that of the population that may beaffected by the condition being studied. Trial sponsors and/or providersmay also lack tools and/or targeting strategies for increasing thediversity of the patient population participating in a clinical trial.

SUMMARY

According to some embodiments of the inventive concept, a methodcomprises: obtaining patient medical record information from a pluralityof providers until a demographic profile of patients associated with thepatient medical record information meets a desired demographic profile;querying the patient medical record information using selection criteriato identify a first subset of patients having first characteristics thatmatch first screening requirements for a clinical trial; identifying,using an artificial intelligence engine, ones of the first subset ofpatients whose medical record information includes secondcharacteristics that match second screening requirements for theclinical trial as clinical trial patient candidates; and communicatingidentities of the clinical trial patient candidates to ones of theplurality of providers that provide healthcare services to the clinicaltrial patient candidates, respectively. The plurality of providers areassociated with a plurality of different organizational managingentities, respectively.

In other embodiments, the demographic profile and the desireddemographic profile are based on social determinants of health (SDOH)factors.

In still other embodiments, the SDOH factors comprise income, number ofemployers, educational level, transportation mode used, proximity topolluted air, proximity to polluted water, language competency, literacystatus, access to nutritious foods, and/or access to physical activityopportunities.

In still other embodiments, the desired demographic profile comprises aplurality of weights corresponding to the SDOH factors, respectively,the plurality of weights representing a relative importance of the SDOHfactors to each other.

In still other embodiments, each of the SDOH factors is representednumerically, the method further comprising: obtaining SDOH factorinformation for the patients associated with the patient medical recordinformation; receiving desired SDOH factor information; determining afirst numeric demographic profile score for the patients associated withthe patient medical record information based on the SDOH factorinformation and the plurality of weights; determining a second numericdemographic profile score based on the desired SDOH factor informationand the plurality of weights; and comparing the first numericdemographic profile score with the second numeric demographic profilescore to determine whether the demographic profile of patientsassociated with the patient medical record information meets the desireddemographic profile.

In other embodiments, the first screening requirements define individualthresholds for one or more of the selection criteria and a matchingthreshold for a number of the individual thresholds that must be met tomatch.

In still other embodiments, the selection criteria comprise demographicinformation, one or more diagnosis codes, laboratory test values,medication names, scores for cognitive tests, medical professionalobservations, acute condition names, chronic condition names, and/orallergy names.

In still other embodiments, the plurality of providers is a firstplurality of providers, the method further comprising: receiving patientclaim information associated with a second plurality of providers, anumber of the second plurality of providers being greater than a numberof the first plurality of providers; and querying the patient claiminformation of each of the second plurality of providers usingdemographic information, one or more diagnosis codes, and/or pharmacyinformation to identify the first plurality of providers having patientswith third characteristics that match third screening requirements forthe clinical trial.

In still other embodiments, the method further comprises receiving acommunication from each of the first plurality of providers opting in toparticipating in the clinical trial.

In still other embodiments, the method further comprises discarding onesof the first plurality of providers having a number of patients with thethird characteristics that match the third screening requirements thatdoes not exceed a threshold.

In still other embodiments, the method further comprises identifying thefirst plurality of providers based on geographic information for thefirst plurality of providers obtained from the patient claiminformation.

In still other embodiments, the second screening requirements compriseclinical characteristics associated with subject matter of the clinicaltrial. Identifying, using the artificial intelligence engine, ones ofthe first subset of patients whose medical record information includessecond characteristics that match second screening requirements for theclinical trial as clinical trial patient candidates comprises:determining, using a machine learning engine or a multi-layer neuralnetwork, whether medical record chart data for the first subset ofpatients matches any of the clinical characteristics associated with thesubject matter of the clinical trial; and determining, using a contentsimilarity engine, whether free-text written by a health carepractitioner contained in the medical record chart data for the firstsubset of patients matches any of the clinical characteristicsassociated with the subject matter of the clinical trial.

In still other embodiments, the second screening requirements defineindividual thresholds for one or more of the second characteristics anda matching threshold for a number of the individual thresholds that mustbe met to match.

In still other embodiments, communicating identities of the clinicaltrial patient candidates to ones of the plurality of providers thatprovide healthcare services to the clinical trial patient candidates,respectively, comprises: providing access to the identities of ones ofthe clinical trial patient candidates receiving healthcare services fromone of the plurality of providers via a networked results portalaccessible by the one of the plurality of providers.

In still other embodiments, communicating identities of the clinicaltrial patient candidates to ones of the plurality of providers thatprovide healthcare services to the clinical trial patient candidates,respectively, comprises: asynchronously transmitting a communicationcontaining the identities of ones of the clinical trial patientcandidates receiving healthcare services from one of the plurality ofproviders to the one of the plurality of providers.

In some embodiments of the inventive concept, a system comprises aprocessor; and a memory coupled to the processor and comprising computerreadable program code embodied in the memory that is executable by theprocessor to perform operations comprising: obtaining patient medicalrecord information from a plurality of providers until a demographicprofile of patients associated with the patient medical recordinformation meets a desired demographic profile; querying the patientmedical record information using selection criteria to identify a firstsubset of patients having first characteristics that match firstscreening requirements for a clinical trial; identifying, using anartificial intelligence engine, ones of the first subset of patientswhose medical record information includes second characteristics thatmatch second screening requirements for the clinical trial as clinicaltrial patient candidates; and communicating identities of the clinicaltrial patient candidates to ones of the plurality of providers thatprovide healthcare services to the clinical trial patient candidates,respectively. The plurality of providers are associated with a pluralityof different organizational managing entities, respectively.

In further embodiments, the demographic profile and the desireddemographic profile are based on social determinants of health (SDOH)factors; and the SDOH factors comprise income, number of employers,educational level, transportation mode used, proximity to polluted air,proximity to polluted water, language competency, literacy status,access to nutritious foods, and/or access to physical activityopportunities.

In still further embodiments, the desired demographic profile comprisesa plurality of weights corresponding to the SDOH factors, respectively,the plurality of weights representing a relative importance of the SDOHfactors to each other.

In still further embodiments, each of the SDOH factors is representednumerically, the operations further comprising: obtaining SDOH factorinformation for the patients associated with the patient medical recordinformation; receiving desired SDOH factor information; determining afirst numeric demographic profile score for the patients associated withthe patient medical record information based on the SDOH factorinformation and the plurality of weights; determining a second numericdemographic profile score based on the desired SDOH factor informationand the plurality of weights; and comparing the first numericdemographic profile score with the second numeric demographic profilescore to determine whether the demographic profile of patientsassociated with the patient medical record information meets the desireddemographic profile.

In some embodiments of the inventive concept, a computer programproduct, comprises a non-transitory computer readable storage mediumcomprising computer readable program code embodied in the medium that isexecutable by a processor to perform operations comprising: obtainingpatient medical record information from a plurality of providers until ademographic profile of patients associated with the patient medicalrecord information meets a desired demographic profile; querying thepatient medical record information using selection criteria to identifya first subset of patients having first characteristics that match firstscreening requirements for a clinical trial; identifying, using anartificial intelligence engine, ones of the first subset of patientswhose medical record information includes second characteristics thatmatch second screening requirements for the clinical trial as clinicaltrial patient candidates; and communicating identities of the clinicaltrial patient candidates to ones of the plurality of providers thatprovide healthcare services to the clinical trial patient candidates,respectively. The plurality of providers are associated with a pluralityof different organizational managing entities, respectively.

It is noted that aspects described with respect to one embodiment may beincorporated in different embodiments although not specificallydescribed relative thereto. That is, all embodiments and/or features ofany embodiments can be combined in any way and/or combination. Moreover,other methods, systems, articles of manufacture, and/or computer programproducts according to embodiments of the inventive concept will be orbecome apparent to one with skill in the art upon review of thefollowing drawings and detailed description. It is intended that allsuch additional systems, methods, articles of manufacture, and/orcomputer program products be included within this description, be withinthe scope of the present inventive subject matter and be protected bythe accompanying claims. It is further intended that all embodimentsdisclosed herein can be implemented separately or combined in any wayand/or combination.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features of embodiments will be more readily understood from thefollowing detailed description of specific embodiments thereof when readin conjunction with the accompanying drawings, in which:

FIG. 1 is a block diagram that illustrates a communication networkincluding an Artificial Intelligence (AI) assisted clinical trialrecruitment system for coordinated identification of patients based onsocial determinants of health (SDOH) information in accordance with someembodiments of the inventive concept;

FIG. 2 is a block diagram of an AI engine incorporating a multi-layerneural network used in the AI assisted clinical trial recruitment systemof FIG. 1 in accordance with some embodiments of the inventive concept;

FIG. 3 is a block diagram of an AI engine incorporating a machinelearning system used in the AI assisted clinical trial recruitmentsystem of FIG. 1 in accordance with some embodiments of the inventiveconcept;

FIGS. 4-7 are flowcharts that illustrate operations for coordinatedidentification of patients based on SDOH information using the AIassisted clinical trial recruitment system of FIG. 1 in accordance withsome embodiments of the inventive concept;

FIG. 8 is a data processing system that may be used to implement one ormore servers in the AI assisted clinical trial recruitment system ofFIG. 1 in accordance with some embodiments of the inventive concept; and

FIG. 9 is a block diagram that illustrates a software/hardwarearchitecture for use in the AI assisted clinical trial recruitmentsystem of FIG. 1 in accordance with some embodiments of the inventiveconcept.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth to provide a thorough understanding of embodiments of the presentinventive concept. However, it will be understood by those skilled inthe art that the present invention may be practiced without thesespecific details. In some instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the present inventive concept. It is intended that allembodiments disclosed herein can be implemented separately or combinedin any way and/or combination. Aspects described with respect to oneembodiment may be incorporated in different embodiments although notspecifically described relative thereto. That is, all embodiments and/orfeatures of any embodiments can be combined in any way and/orcombination.

Embodiments of the inventive concept are described herein in the contextof an artificial intelligence engine comprising a multi-layer neuralnetwork, a content similarity engine, which includes a natural languageprocessor, and/or a machine learning system. It will be understood thatother types of artificial intelligence systems can be used in otherembodiments of the artificial intelligence engine including, but notlimited to deep learning systems, and/or computer vision systems.Moreover, it will be understood that the multi-layer neural networkdescribed herein is a multi-layer artificial neural network comprisingartificial neurons or nodes and does not include a biological neuralnetwork comprising real biological neurons.

Some embodiments of the inventive concept stem from a realization that amajor factor delaying many clinical trials is the inability to recruitand enroll a sufficient number of patients as participants. Variousapproaches have been taken to encourage patients to enroll in trials,which typically involve analyzing anonymous patient data to determinelocations, e.g., geographic locations containing provider facilitiesthat appear to serve many of the type of patient sought for a particularclinical trial and then attempting to reach these patients and encouragethem to enroll in the trial. This often involves social media campaigns,targeted mailings, advertisements, and the like aimed at potentialenrollees. Such approaches rely on patients to contact their health careproviders (e.g., physician or other professional) to request that theybe evaluated for participation in a trial. Thus, to enroll a patient ina trial, they must be successfully notified of the existence of thetrial through an outreach campaign that convinces the patient to contacttheir health care provider to request participation in the trial. Then,the health care provider must determine that the patient is indeedqualified and recommend the patient as a candidate for the trial. Due tothe reliance on patients taking the initiative to drive their enrollmentinto a clinical trial, it is difficult to recruit patients in sufficientnumbers to staff many trials. Moreover, the demographics of theparticipants that do enroll may not reflect that of the generalpopulation or that of the population that may be affected by thecondition being studied. When clinical trials do not reflect thedemographics of the overall population or the demographics of thepopulation affected by the condition being studied, then the trialresults may not be useful or applicable.

Some embodiments of the inventive concept may provide an ArtificialIntelligence (AI) assisted clinical trial recruitment system, whichinvolves obtaining patient medical records until a demographic profileof the patients meets a desired patient demographic profile. Thedemographic profile of the patients associated with the medical recordsand a desired patient demographic profile, i.e., a demographic profilethat would be appropriate or desirable based on the subject matter ofthe clinical trial may be based on social determinants of health (SDOH)factors, which may contribute to wide disparities in the health ofindividuals. These factors may include, but are not limited to, income,number of employers, educational level, transportation mode used,proximity to polluted air, proximity to polluted water, languagecompetency, literacy status, access to nutritious foods, and/or accessto physical activity opportunities. These SDOH factors may affectpatient health in a variety of ways. For example, lack of transportationand/or convenient locations for receiving health care may impact thetype and sufficiency of healthcare a person receives. Working multiplejobs may inhibit an individual from maintaining routine and/orpreventive care due to the inability to schedule appointments withhealthcare professionals. SDOH factors may also affect patientwillingness to participate in clinical trials. Certain demographicgroups historically have had a mistrust of the healthcare system andeducation gaps often propagate this mistrust. In some embodiments, thedesired demographic profile may include a plurality of weights thatcorrespond to the SDOH factors, respectively. These weights may specifythe relative importance of the SDOH factors to each other. SDOH factorinformation may be obtained for both the patients associated with themedical records (i.e., prospective trial participants) and desired SDOHfactor information for the trial. The SDOH factors may be representednumerically by transforming those factors that are categorical, e.g.,literacy status, language competency, etc., through an embedding thatmaps the categorical information to a vector of continuous numbers.Numeric demographic profile scores may then be determined or generatedfor both the patients and the desired demographic profile for the trial.These scores may then be compared to determine whether the demographicprofile of the patients meets the desired demographic profile for thetrial.

Once a patient population has been identified that meets a desireddemographic profile for the trial, the patient medical records for thatidentified patient population can then be processed and analyzed toidentify clinical trial patient candidates therefrom. In someembodiments, the medical record information is queried using selectioncriteria to identify a first subset of patients that have firstcharacteristics that match first screening requirements for the clinicaltrial. A match determination may be made based on a threshold analysisfor individual ones of the selection criteria and the number ofthresholds that must be met in total to constitute a match. In someembodiments, the selection criteria may comprise demographicinformation, one or more diagnosis codes, and/or medical historyinformation. For example, these selection criteria may include, but arenot limited to, laboratory test values, medication names, scores forcognitive tests, medical professional observations, acute conditionnames, chronic condition names, and/or allergy names. An artificialintelligence engine may be used to identify those patients within thefirst subset of patients whose medical information includes secondcharacteristics that match second screening requirements for theclinical trial. A match determination may be made on selected medicalinformation including free-text generated by a health care practitionerin serving the patient. Similar to the first match determination, thesecond match determination may be made based on a threshold analysis forindividual ones of the second screening requirements and the number ofthresholds that must be met in total to constitute a match. Thosepatients that are determined to be matched based on the second screeningrequirements for the clinical trial may be identified as clinical trialpatient candidates. The identities of these clinical trial patientcandidates may then be communicated to the various providers thatprovide healthcare services to the patients to encourage these providersto make their own determination whether their patients would benefit byparticipating in the clinical trial and then consulting with thesepatients about enrolling in the trial. The providers may be associatedwith different organizational management entities, i.e., the health careproviders do not share any common management. The identities of theclinical trial patient candidates may be communicated to the providersasynchronously by transmitting communications to the providers. In otherembodiments, if the provider, for example, has an onsite trialcoordinator, then the identities of the trial patient candidates may becommunicated through a networked results portal for the trial that theprovider has access to.

In some embodiments, the plurality of providers may be initiallyidentified through review of patient claim information associated withthe providers and querying the claim information using, for example,de-identified patient information, such as demographic information, oneor more diagnosis codes, and/or pharmacy information. The providers maybe selected using a threshold analysis to ensure that the number oftrial patient candidates associated with the providers is greater thansome lower bound based on the selection criteria used in the query. Forexample, some clinical trials may require a large number of participantsand/or be associated with conditions that are prevalent in society,which may justify excluding some providers that may serve a low numberof patients that may be candidates for such trials. Other clinicaltrials, however, may be associated with rare conditions such thatproviders serving a small number of potential patient candidates maynevertheless still be included among the selected providers. In someembodiments, providers may be geo-targeted using zip-codes or othergeographic identifying indicia to select providers in a particulargeographic area.

Once a provider has been identified as providing health care services topatients that may be potential clinical trial candidates, the providermay opt in to participating in the clinical trial allowing access to theprovider's patients' medical record information. This relationship maycarry forward for multiple trials, but the medical record informationreviewed and processed is discarded and not used in determining patientcandidacy for subsequent trials.

Referring to FIG. 1, a communication network 100 including an AIassisted clinical trial recruitment system for coordinatedidentification of patients based on social determinants of health (SDOH)information, in accordance with some embodiments of the inventiveconcept, comprises multiple health care provider facilities orpractices. Each health care provider facility or practice may representvarious types of organizations that are used to deliver health careservices to patients via health care professionals, which are referredto generally herein as “providers.” The providers may include, but arenot limited to, hospitals, medical practices, mobile patient carefacilities, diagnostic centers, lab centers, and the like. The providersmay operate by providing health care services for patients and theninvoicing one or more payors 160 a and 160 b for the services rendered.The payors 160 a and 160 b may include, but are not limited to, privateinsurance plans, government insurance plans (e.g., Medicare, Medicaid,state or federal public employee insurance plans), hybrid insuranceplans (e.g., Affordable Care Act plans), private medical cost sharingplans, and the patients themselves. Two providers are illustrated inFIG. 1 with the first provider including a first health care facilityserver 105 a coupled to devices 110 a, 110 b, and 110 c via a network115 a. The health care facility server 105 a may be configured with anElectronic Medical Record (EMR) system module 120 a to manage patientfiles and facilitate the entry of orders for patients via health careservice practitioners or personnel. Although shown as one combinedsystem in FIG. 1, it will be understood that some health care facilitiesuse separate systems for electronic medical record management and orderentry management. The providers may use devices, such as devices 110 a,110 b, and 110 c to manage patients' electronic records and to issueorders for the patients through the EMR system 120 a. An order mayinclude, but is not limited to, a treatment, a procedure (e.g., surgicalprocedure, physical therapy procedure, radiologic/imaging procedure,etc.) a test, a prescription, and the like. The network 115 acommunicatively couples the devices 110 a, 110 b, and 110 c to thehealth care facility server 105 a. The network 115 a may comprise one ormore local or wireless networks to communicate with the health carefacility server 105 a when the health care facility server 105 a islocated in or proximate to the health care facility. When the healthcare facility server 105 a is in a remote location from the health carefacility, such as part of a cloud computing system or at a centralcomputing center, then the network 115 a may include one or more widearea or global networks, such as the Internet. The second provider issimilar to the first provider and includes a second health care facilityserver 105 b, which is configured with an EMR system module 120 b.Devices 110 d, 110 e, and 110 f are coupled to the second health carefacility server 105 b via a network 115 b. In accordance with someembodiments of the inventive concept, the providers are associated withdifferent organizational management entities, i.e., the health careproviders do not share any common management.

According to embodiments of the inventive concept, clinical trialcoordinators may use an AI assisted clinical trial recruitment system tocoordinate identification of patients based on SDOH information. The AIassisted clinical trial recruitment system may include a health carefacility/payor interface server 130, which includes an EMR/claimsinterface system module 135 to facilitate the transfer of informationbetween the EMR system 120, which the providers use to manage patientrecords and issue orders, and an AI server 140, which includes an AIengine module 145. The health care facility/payor interface server 130and EMR/claims interface system module 135 are further configured tofacilitate the transfer of claims information for various providers fromthe payors 160 a and 160 b to the AI server 140 and AI engine module145. The AI server 140 and AI engine module 145 may be configured toreceive patient information, provider information, order information,patient diagnoses, lab results, and other patient data contained inrecords in the EMR system 120 from the health care facility server 105and EMR system module 120 by way of the health care facility interfaceserver 130 and EMR/claims interface system module 135. The AI server 140and AI engine module 145 may be further configured to receive claiminformation from the payors 160 a and 160 b and SDOH information forpatients from a SDOH information source 175 by way of the health carefacility interface server 130 and EMR/claims interface system module135. The SDOH information source 175 may be, for example, an entity thatcollects and compiles demographic information for persons including SDOHinformation and makes this information available for purchase by otherorganizations. The AI server 140 and AI engine module 145 may includeboth AI based and non-AI based functionality and may be configured toobtain patient medical records for a patient population that meets adesired patient demographic profile based on SDOH factors. The desiredpatient demographic profile may be based on the demographics of aparticular geographic area and/or the demographics that would berepresentative of individuals that may be affected by the condition(s)being studied in the clinical trial. The AI server 140 and AI enginemodule 145 may be further configured to target individual patients ofproviders associated with different organizational management entitiesthrough querying the medical records of the providers to determinewhether the patients satisfy various screening criteria for the clinicaltrial. The records may be queried through an iterative process withvarious thresholds set to determine whether the characteristics of thepatient's medical records satisfy the trial screening criteria. In someembodiments, the providers may be identified using selection criteriaapplied to the claim data obtained from the payors 160 a and 160 b andassociated with the providers. Once identified, the providers may chooseto opt in to participate in patient recruitment for a current trial aswell as future trials by providing access to patient medical recordseach time for each trial recruitment effort. The AI server 140 and AIengine module 145 may be configured communicate the results of themedical record analysis to the various providers having patients thathave been identified as candidates for the clinical trial. The providersmay then schedule consults with their patients to discuss the option ofparticipating in the trial, which benefits the provider practice throughadditional consult fees as well as improves patient care by providingadditional treatment or therapy options.

A network 150 couples the health care facility servers 105 a and 105 bto the health care facility/payor interface server 130, couples thepayors 160 a and 160 b to the health care facility/payor interfaceserver 130, and couples the SDOH information source 175 to the healthcare facility/payor interface server 130. The network 150 may be aglobal network, such as the Internet or other publicly accessiblenetwork. Various elements of the network 150 may be interconnected by awide area network, a local area network, an Intranet, and/or otherprivate network, which may not be accessible by the general public.Thus, the communication network 150 may represent a combination ofpublic and private networks or a virtual private network (VPN). Thenetwork 150 may be a wireless network, a wireline network, or may be acombination of both wireless and wireline networks.

The service provided through the health care facility interface server130, EMR/claims interface system module 135, AI server 140, and AIengine module 145 to provide AI assisted coordinated identification ofpatients based on SDOH information for clinical trial recruitment may,in some embodiments, be embodied as a cloud service. For example,clinical trial coordinators may access the AI assisted clinical trialrecruitment service as a Web service. In some embodiments, the AIassisted clinical trial recruitment service may be implemented as aRepresentational State Transfer Web Service (RESTful Web service).

Although FIG. 1 illustrates an example communication network includingan AI assisted clinical trail recruitment system for coordinatedidentification of patients based on SDOH information, it will beunderstood that embodiments of the inventive subject matter are notlimited to such configurations, but are intended to encompass anyconfiguration capable of carrying out the operations described herein.

The AI engine 145 may be embodied in a variety of ways including, forexample, but not limited to a multi-layer neural network, a naturallanguage processing system, and a machine learning system.

FIG. 2 is a block diagram of the AI engine 145 used in the AI assistedclinical trial recruitment system in accordance with some embodiments ofthe inventive concept. As shown in FIG. 2, the AI engine 145 may includea medical record chart data module 205, a multi-layer neural network210, a content similarity engine 215, and a combiner 220 that areconnected as shown. The medical record chart data module 205 may beconfigured to receive information associated with a patient, informationassociated with a provider, information associated with an order for thepatient, and any other information included in a patient's medicalrecord including lab results, radiology images, physician or otherhealth care practitioner free-text notes, etc. from, for example, ahealth care provider facility by way of the health care facilityinterface server 130 and EMR/claims interface system module 135. Thepatient information may include, but is not limited to, age, gender,problem list (e.g., description of one or more ailments or conditionsthe patient is suffering from), encounter diagnosis (the issue thatcauses a patient to visit the health care provider), patient class(e.g., in-patient or out-patient), and/or a medical center department(e.g., emergency room, cardiology, radiology, etc.). Note that apatient's encounter diagnosis may be different than a patient's problemlist. For example, a patient may fall and receive a head injury, whichmay result in an encounter diagnosis of head trauma. The patient maynevertheless have a problem list description that includes heart diseaseand arthritis. The provider information may include, but is not limitedto, a provider identifier and/or a provider specialty (e.g., cardiology,oncology, etc.). The order information may include, but is not limitedto, an order name, order identification, order modality, order contrast,body area identification and/or a free-text reason for the order.

The medical record chart data module 205 may be configured to organizeall of this information with the exception of the free-text reason forthe order(s) or any free-text notes written into the patient's record byone or more health care practitioners into an input data set for theneural network 210.

The medical record chart data module 205 may be further configured toprocess any free-text input that may have been entered by a provider togenerate a clinical input text for the content similarity engine 215. Insome embodiments, the free-text reason input entered by the provider maybe combined with additional information, such as a patient's encounterdiagnosis and/or identification of an affected body area, to create theclinical input text that may capture clinical aspects of the reason foran order. The body area information may be obtained from the order thatis being placed. For example, a computed tomography (CT) head examindicates that the affected body area is the patient's head.

The neural network 210 may comprise multiple layers including a firstfeaturization layer 220, a second featurization layer 225, one or moreclassification layer(s) 230, and an output layer 235. The first andsecond featurization layers 220 and 225 may be configured toautomatically perform feature extraction on the input data set from themedical record chart data module 205 to reduce the dimensionalitythereof so as to allow the neural network 210 to learn an efficientrepresentation of the input data. According to some embodiments, thefirst featurization layer may be configured to numerically encode thecategorical value information to create a categorical value informationvocabulary, to embed the numerically encoded categorical valueinformation into a categorical value information input vector, tonumerically encode the sequence of categorical values information tocreate a sequence of categorical values vocabulary, and to embed thenumerically encoded sequence of categorical values information into asequence of categorical values information input vector. The encodingand embedding processes may comprise representing discrete numbers by avector of continuous values representing a meaningful aspect of theinput data set. The second featurization layer 225 may be configured toconcatenate the scaled numerical value information output from themedical record chart data module 205 with the categorical valueinformation input vector, and the sequence of categorical valuesinformation input vector. This concatenation may be viewed as a fullrepresentation of the input data set. One or more classificationlayer(s) 230 may be configured to perform supervised learning ofcorrelations between the input data set, as represented by the vectorand scaled numerical value information concatenation output from thesecond featurization layer 225, and clinical trial selection criteria orscreening requirements.

The content similarity engine 215 may be configured to receive theclinical input text including free-text generated by one or more healthcare practitioners. A natural language processor module 240 may beconfigured to tokenize both the clinical input text and the clinicaltrial selection criteria or screening requirements into sequences ofwords to create a clinical input vocabulary and a clinical trialcriteria/screening vocabulary, respectively. As part of this process,spelling errors may be corrected, synonyms may be resolved, and amaximum sequence length may be defined. Words may be weighted by howimportant they are based on their presence in the individual segment oftext as well as in the full data set using, for example, a processcalled term frequency-inverse document frequency (td-idf) weighting.This may reduce the impact of common words used throughout the data setand may increase the impact of words specific to a segment of text. Thenatural language processor 240 may generate an encoded and embeddedclinical input vector from the clinical input text and may generate aplurality of encoded and embedded clinical trial selectioncriteria/screening requirement vectors. The dot-product of the clinicalinput vector with each of the plurality of clinical trial selectioncriteria/screening requirement vectors may be used as a measure ofsimilarity of the original free-text reason input with each of theclinical trial selection criteria/screening requirements. If there areno words in common, then the dot-product would be zero. If the match isperfect, then the dot-product would be one. These dot-product values maybe used as match scores for the clinical trial selectioncriteria/screening requirements.

The combiner 220 may receive the correlations between the input data setand clinical trial selection criteria or screening requirements outputfrom the output layer 235 along with the match scores output by thecontent similarity engine 215. The combiner 220 may merge thisinformation to evaluate whether particular elements of a patient'smedical record data match various clinical trial selection criteria andscreening requirements to satisfy defined thresholds for each individualclinical trial criterion or screening requirement and by the number ofthresholds satisfied.

A determination may then be made whether there is a patient match atblock 245 based on the threshold analysis performed with respect to thepatient medical record data and the clinical trial selection criteriaand screening requirements.

FIG. 3 is a block diagram of the AI engine 145 incorporating a machinelearning system in accordance with some embodiments of the inventiveconcept.

As shown in FIG. 3, the AI engine module 145 may include both trainingmodules and modules used for processing new data on which to identifypatients for participation in a clinical trial. The modules used in thetraining portion of the AI engine module 145 include the training data305, the featuring module 325, the labeling module 330, and the machinelearning engine 340. The training data 305 may comprise informationassociated with historical medical record chart data as described abovewith respect to the medical record chart data 205 of FIG. 2. Thetraining data 305 may also include examples of clinical trial selectioncriteria and/or screening requirements. The featuring module 325 isconfigured to identify the individual independent variables that areused by the AI engine module 145 to determine a match betweencharacteristics of a patient's medical record data and one or moreclinical trial selection criteria and/or screening requirements, whichmay be considered a dependent variable. For example, the training data305 may be generally unprocessed or formatted and include extrainformation in addition to medical claim information and clinical trialselection criteria and/or screening requirements. For example, themedical record data may include account codes, business addressinformation, and the like, which can be filtered out by the featuringmodule 325. The features extracted from the training data 305 may becalled attributes and the number of features may be called thedimension. The labeling module 330 may be configured to assign definedlabels to the training data and to clinical trial patient matchdeterminations to ensure a consistent naming convention for both theinput features and the generated outputs. The machine learning engine340 may process both the featured training data 305, including thelabels provided by the labeling module 330, and may be configured totest numerous functions to establish a quantitative relationship betweenthe featured and labeled input data and the generated outputs. Themachine learning engine 340 may use modeling techniques to evaluate theeffects of various input data features on the generated outputs. Theseeffects may then be used to tune and refine the quantitativerelationship between the featured and labeled input data and thegenerated outputs. The tuned and refined quantitative relationshipbetween the featured and labeled input data generated by the machinelearning engine 240 is output for use in the AI engine 345. The machinelearning engine 340 may be referred to as a machine learning algorithm.

The modules used for processing new data on which to determine whethercharacteristics of a patient's medical record data match one or moreselection criteria and/or screening requirements for a clinical trialinclude the new data 355, the featuring module 365, the AI engine module345, and the patient match module 375. The new data 355 may be the samedata/information as the training data 305 in content and form except thedata will be used for an actual determination of whether a patient'smedical record data match the clinical trial selection criteria and/orscreening requirements using a thresholding analysis as described above.Likewise, the featuring module 365 performs the same functionality onthe new data 355 as the featuring module 325 performs on the trainingdata 305. The AI engine 345 may, in effect, be generated by the machinelearning engine 340 in the form of the quantitative relationshipdetermined between the featured and labeled input data and the generatedoutputs. The AI engine 345 may, in some embodiments, be referred to asan AI model. The AI engine 345 may be configured to output adetermination of whether a patient's medical record information matcheswith the clinical trial selection criteria and/or screening requirementsbased on a threshold analysis via the patient match module 375. Thepatient match module 375 may be configured to communicate theidentification of the patient as a clinical trial patient candidate tothe provider that serves the patient. As described above, the identityof a clinical trial patient candidate may be communicated to theprovider asynchronously by transmitting a communication to the provider(e.g., an email, call, letter, or the like). In other embodiments, ifthe provider, for example, has an onsite trial coordinator, then theidentity of the trial patient candidate may be communicated through anetworked results portal for the trial that the provider has access to.

FIGS. 4-7 are flowcharts that illustrate operations for coordinatedidentification of patients based on SDOH information using the AIassisted clinical trial recruitment system of FIG. 1 in accordance withsome embodiments of the inventive concept. Referring now to FIG. 4,operations begin at block 400 where the AI server 140 may obtain patientmedical record information from each of a plurality of providers until ademographic profile of the patients associated with the medical recordinformation meets a desired demographic profile. The providers may beassociated with a plurality of different organizational managingentities, respectively. Example embodiments for obtaining a desiredpatient demographic profile from which to draw clinical trial candidatesfrom are illustrated in FIG. 5.

Referring now to FIG. 5, SDOH factor information may be obtained forpatients for whose medical record information is available through theproviders at block 500. These factors may include, but are not limitedto, income, number of employers, educational level, transportation modeused, proximity to polluted air, proximity to polluted water, languagecompetency, literacy status, access to nutritious foods, and/or accessto physical activity opportunities. The SDOH factor information may beobtained, for example, from the SDOH information source 17 of FIG. 1.Desired SDOH factor information may be received at block 505 from, forexample, a clinical trial administrator. The desired SDOH factorinformation may be representative of desired demographics of thepatients to be included in a clinical trial based on geography,demographics of the general population, and/or demographics of patientsthat are relevant to the subject matter or the condition(s) beingstudied in the clinical trial. In some embodiments, the desireddemographic profile may include a plurality of weights that correspondto the SDOH factors, respectively. These weights may specify therelative importance of the SDOH factors to each other. At block 510, afirst numeric demographic profile score for the patients associated withthe medical record information may be determined based on the SDOHfactor information and the plurality of weights. For example, in someembodiments, the first numerical profile score may be determined bymapping those SDOH factors that are categorical or discrete into vectorsof continuous numbers using an embedding. The first numeric demographicprofile score may be determined based on a summation of the products ofthe weights and the numeric values for the various SDOH factors. Insimilar fashion to determining the first demographic profile score, asecond demographic profile score may be determined at block 515 based onthe desired SDOH factor information and the plurality of weights. Thefirst numeric demographic profile score and the second numericdemographic profile score may be compared at block 520 to determinewhether the demographic profile of the patients meets the desireddemographic profile for the trial.

Based on the operations of FIG. 5, for example, a patient population maybe identified that meets a desired demographic profile for a clinicaltrial. The patient medical records for that identified patientpopulation can then be processed and analyzed to identify clinical trialpatient candidates therefrom. Returning to FIG. 4, at block 405, thepatient medical record information is queried using selection criteriato identify a first subset of patients that have characteristics thatmatch the first screening requirements for a clinical trial. Theselection criteria may include, but is not limited to, patientdemographic information, one or more diagnoses codes, and/or medicalhistory information. these selection criteria may include, but are notlimited to, laboratory test values, medication names, scores forcognitive tests, medical professional observations, acute conditionnames, chronic condition names, and/or allergy names. The AI engine mayuse the neural network 210, the content similarity engine 215, and/orthe machine learning system of FIG. 3 to identify ones of the firstsubset of patients whose medical record information includes secondcharacteristics that match second screening requirements for theclinical trial at block 410.

The first screening requirements may define individual thresholds forone or more of the selection criteria and a matching threshold for anumber of the individual thresholds that must be met to be considered amatch. Likewise, the second screening requirements may define individualthresholds for one or more of the second characteristics and a matchingthreshold for a number of the individual thresholds that must be met tobe considered match. The operations of block 410 may be repeated initerative fashion to compare ever more data from the medical recordinformation with more detailed and granular screening requirements forthe clinical trial. Once one or more clinical trial patient candidateshave been identified at block 410, the identities of these patients maybe communicated to their corresponding provider(s) at block 415. In someembodiments, the SDOH factor information associated with the patientsidentified as clinical trial patient candidates at block 410 may beprocessed using the operations described above with respect to FIG. 5 toensure that these patients meet the desired demographic profile, basedon SDOH information, for the clinical trial. If the demographic profileof the clinical trial patient candidates based on SDOH information doesnot meet the desired demographic profile for the clinical trial, thenthe operations of blocks 400, 405, and 410 may be repeated in iterativefashion until both the patient demographic requirements, based on SDOHinformation, and the screening requirements from the patient medicalrecords are met. As described above, the identity of a clinical trialpatient candidate may be communicated to the provider asynchronously bytransmitting a communication to the provider (e.g., an email, call,letter, or the like). In other embodiments, if the provider, forexample, has an onsite trial coordinator, then the identity of the trialpatient candidate may be communicated through a networked results portalfor the trial that the provider has access to. Patient data that isaccessed for determining trial candidacy may be securely discarded afterthe process of identifying patients for a clinical trial is complete.This data may not used for other purposes or saved for identifyingpatients for other clinical trials to ensure compliance with medicalrecord handling and security laws and regulations.

Referring now to FIG. 6, embodiments for identifying the plurality ofproviders begin at block 600 where patient claim information is receivedat the AI server 140 for a second larger plurality or providers. Thepatient claim information received from one or more payors is queried atblock 605 using, for example, selection criteria based on de-identifiedpatient information, such as demographic information, one or morediagnosis codes, and/or pharmacy information. This query of the patientclaim information may identify those patients of the providers that havethird characteristics that match third screening requirements for theclinical trial. The third screening requirements may define individualthresholds for one or more of the third characteristics and a matchingthreshold for a number of the individual thresholds that must be met tobe considered a match in similar fashion to the thresholding describedabove with respect to the medical record information. Based on thisquery, the first plurality of providers may be identified from thelarger second plurality of providers for which the claim information wasobtained. In some embodiments, a further threshold analysis may beperformed to ensure that each of the first plurality of providersprovides health care services to or is otherwise associated with anumber of patient candidates for the clinical trial that is greater thana defined lower bound based on the selection criteria used in the query.If an identified provider is associated with too few patient candidatesfor the clinical trial based on the claim information query, then theprovider may be excluded from the identified first plurality ofproviders whose patients' medical record information is further analyzedas described above to identify patient candidates for the clinicaltrial. The selection criteria and/or the patient candidate threshold maybe modified and the querying operation of block 605 may be repeateduntil a number of providers that each serve a number of patientcandidates for the clinical trial that is greater than the defined lowerbound are identified to increase the likelihood that a sufficient numberof patients may be identified as candidates for the clinical trial. Insome embodiments, providers may be geo-targeted using zip-codes or othergeographic identifying indicia from the patient claim information toidentify or select providers in a particular geographic area. Thus,according to some embodiments of the inventive concept, payor claiminformation may be reviewed and queried to determine those providersthat are likely to have patients that may be good candidates for aclinical trial.

In accordance with various embodiments of the inventive concept,multiple types of AI technology may be used in the clinical trialrecruitment system for coordinated identification of patients served bymultiple providers. Referring now to FIG. 7, the operations of block 410may be performed beginning at block 700 where a machine learning engine,such as that described above with respect to FIG. 3 or a multi-layerneural network, such as the neural network 210 of FIG. 2, may be used todetermine whether medical record chart data for the first subset ofpatients (i.e., those patients that have been identified using theinitial clinical trial selection criteria at block 405 of FIG. 4)matches any of the clinical characteristics associated with the subjectmatter of the clinical trial. At block 705, a content similarity engine,such as the content similarity engine 215 of FIG. 2 is used to determinewhether free-text written by a health care practitioner contained in themedical record chart data for the first subset of patients matches anyof the clinical characteristics associated with the subject matter ofthe clinical trial.

Referring now to FIG. 8, a data processing system 800 that may be usedto implement the AI server 140 of FIG. 1, in accordance with someembodiments of the inventive concept, comprises input device(s) 802,such as a keyboard or keypad, a display 804, and a memory 806 thatcommunicate with a processor 808. The data processing system 800 mayfurther include a storage system 810, a speaker 812, and an input/output(I/O) data port(s) 814 that also communicate with the processor 808. Theprocessor 808 may be, for example, a commercially available or custommicroprocessor. The storage system 810 may include removable and/orfixed media, such as floppy disks, ZIP drives, hard disks, or the like,as well as virtual storage, such as a RAMDISK. The I/O data port(s) 814may be used to transfer information between the data processing system800 and another computer system or a network (e.g., the Internet). Thesecomponents may be conventional components, such as those used in manyconventional computing devices, and their functionality, with respect toconventional operations, is generally known to those skilled in the art.The memory 806 may be configured with computer readable program code 816to facilitate AI assisted clinical trial recruitment according to someembodiments of the inventive concept.

FIG. 9 illustrates a memory 905 that may be used in embodiments of dataprocessing systems, such as the AI server 140 of FIG. 1, the health carefacility interface server 130 of FIG. 1, and the data processing system800 of FIG. 8, respectively, to facilitate AI assisted clinical trialrecruiting according to some embodiments of the inventive concept. Thememory 905 is representative of the one or more memory devicescontaining the software and data used for facilitating operations of theAI server 140 and AI engine module 145 as described herein. The memory905 may include, but is not limited to, the following types of devices:cache, ROM, PROM, EPROM, EEPROM, flash, SRAM, and DRAM. As shown in FIG.9, the memory 905 may contain three or more categories of softwareand/or data: an operating system 910, a demographic profile validationmodule 915, an AI engine module 920, and a communication module 950. Inparticular, the operating system 910 may manage the data processingsystem's software and/or hardware resources and may coordinate executionof programs by the processor. The demographic profile validation module915 may be configured to perform one or more of the operations describedabove with respect to FIGS. 4 and 5. The AI engine module 920 mayinclude a machine learning module 925, a neural network module 930, acontent similarity engine module 935, and a combiner module 940. Themachine learning module 925 may be configured to perform one or more ofthe operations described above with respect to FIG. 3 and the flowchartsof FIGS. 4-7. The neural network module 930 may be configured to performone or more of the operations described above with respect to the neuralnetwork 210 of FIG. 2 and the flowcharts of FIGS. 4-7. The contentsimilarity engine module 935 may be configured to perform one or more ofthe operations described above with respect to the content similarityengine 215 of FIG. 2 and the flowcharts of FIGS. 4-7. The combinermodule 940 may be configured to perform one or more of the operationsdescribed above with respect to the combiner 220 of FIG. 2 and theflowcharts of FIGS. 4-7. The communication module 950 may be configuredto support communication between, for example, the AI server 140 and thehealth care facility interface server 130, between the health carefacility interface server 130 and the health care facility servers 105a, 105 b, and between the health care facility interface server 130 andthe payors 160 a, 160 b.

Although FIGS. 8-9 illustrate hardware/software architectures that maybe used in data processing systems, such as the AI server 140 of FIG. 1,the health care facility interface server 130 and the data processingsystem 800 of FIG. 8, respectively, in accordance with some embodimentsof the inventive concept, it will be understood that embodiments of thepresent invention are not limited to such a configuration but isintended to encompass any configuration capable of carrying outoperations described herein.

Computer program code for carrying out operations of data processingsystems discussed above with respect to FIGS. 1-9 may be written in ahigh-level programming language, such as Python, Java, C, and/or C++,for development convenience. In addition, computer program code forcarrying out operations of the present invention may also be written inother programming languages, such as, but not limited to, interpretedlanguages. Some modules or routines may be written in assembly languageor even micro-code to enhance performance and/or memory usage. It willbe further appreciated that the functionality of any or all of theprogram modules may also be implemented using discrete hardwarecomponents, one or more application specific integrated circuits(ASICs), or a programmed digital signal processor or microcontroller.

Moreover, the functionality of the AI server 140 of FIG. 1, the healthcare facility interface server 130 of FIG. 1, and the data processingsystem 800 of FIG. 8 may each be implemented as a single processorsystem, a multi-processor system, a multi-core processor system, or evena network of stand-alone computer systems, in accordance with variousembodiments of the inventive concept. Each of these processor/computersystems may be referred to as a “processor” or “data processing system.”

The data processing apparatus described herein with respect to FIGS. 1-9may be used to facilitate AI assisted clinical trial recruitment basedon SDOH information according to some embodiments of the inventiveconcept described herein. These apparatus may be embodied as one or moreenterprise, application, personal, pervasive and/or embedded computersystems and/or apparatus that are operable to receive, transmit, processand store data using any suitable combination of software, firmwareand/or hardware and that may be standalone or interconnected by anypublic and/or private, real and/or virtual, wired and/or wirelessnetwork including all or a portion of the global communication networkknown as the Internet, and may include various types of tangible,non-transitory computer readable media. In particular, the memory 905when coupled to a processor includes computer readable program codethat, when executed by the processor, causes the processor to performoperations including one or more of the operations described herein withrespect to FIGS. 1-7.

Some embodiments of the inventive concept may provide an AI assistedclinical trial recruitment system in which SDOH factors are taken intoaccount in compiling patient candidates to draw from for populating aclinical trial. By tailoring the SDOH based demographic profile of thepool of patients that are used to draw candidates for a clinical trialtowards a desired SDOH demographic profile that is applicable to theclinical trial subject matter, the clinical trial may generate improvedand more relevant results that can be used to evaluate the condition(s)being studied.

FURTHER DEFINITIONS AND EMBODIMENTS

In the above description of various embodiments of the present inventiveconcept, it is to be understood that the terminology used herein is forthe purpose of describing particular embodiments only and is notintended to be limiting of the invention. Unless otherwise defined, allterms (including technical and scientific terms) used herein have thesame meaning as commonly understood by one of ordinary skill in the artto which this inventive concept belongs. It will be further understoodthat terms, such as those defined in commonly used dictionaries, shouldbe interpreted as having a meaning that is consistent with their meaningin the context of this specification and the relevant art and will notbe interpreted in an idealized or overly formal sense expressly sodefined herein.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousaspects of the present inventive concept. In this regard, each block inthe flowchart or block diagrams may represent a module, segment, orportion of code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the inventiveconcept. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items. Like reference numbers signify like elementsthroughout the description of the figures.

In the above-description of various embodiments of the present inventiveconcept, aspects of the present inventive concept may be illustrated anddescribed herein in any of a number of patentable classes or contextsincluding any new and useful process, machine, manufacture, orcomposition of matter, or any new and useful improvement thereof.Accordingly, aspects of the present inventive concept may be implementedentirely hardware, entirely software (including firmware, residentsoftware, micro-code, etc.) or combining software and hardwareimplementation that may all generally be referred to herein as a“circuit,” “module,” “component,” or “system.” Furthermore, aspects ofthe present inventive concept may take the form of a computer programproduct comprising one or more computer readable media having computerreadable program code embodied thereon.

Any combination of one or more computer readable media may be used. Thecomputer readable media may be a computer readable signal medium or acomputer readable storage medium. A computer readable storage medium maybe, for example, but not limited to, an electronic, magnetic, optical,electromagnetic, or semiconductor system, apparatus, or device, or anysuitable combination of the foregoing. More specific examples (anon-exhaustive list) of the computer readable storage medium wouldinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an appropriateoptical fiber with a repeater, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

The description of the present inventive concept has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the inventive concept in the form disclosed.Many modifications and variations will be apparent to those of ordinaryskill in the art without departing from the scope and spirit of theinventive concept. The aspects of the inventive concept herein werechosen and described to best explain the principles of the inventiveconcept and the practical application, and to enable others of ordinaryskill in the art to understand the inventive concept with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A method, comprising: obtaining patient medicalrecord information from a plurality of providers until a demographicprofile of patients associated with the patient medical recordinformation meets a desired demographic profile; querying the patientmedical record information using selection criteria to identify a firstsubset of patients having first characteristics that match firstscreening requirements for a clinical trial; identifying, using anartificial intelligence engine, ones of the first subset of patientswhose medical record information includes second characteristics thatmatch second screening requirements for the clinical trial as clinicaltrial patient candidates; and communicating identities of the clinicaltrial patient candidates to ones of the plurality of providers thatprovide healthcare services to the clinical trial patient candidates,respectively; wherein the plurality of providers are associated with aplurality of different organizational managing entities, respectively.2. The method of claim 1, wherein the demographic profile and thedesired demographic profile are based on social determinants of health(SDOH) factors.
 3. The method of claim 2, wherein the SDOH factorscomprise income, number of employers, educational level, transportationmode used, proximity to polluted air, proximity to polluted water,language competency, literacy status, access to nutritious foods, and/oraccess to physical activity opportunities.
 4. The method of claim 3,wherein the desired demographic profile comprises a plurality of weightscorresponding to the SDOH factors, respectively, the plurality ofweights representing a relative importance of the SDOH factors to eachother.
 5. The method of claim 4, wherein each of the SDOH factors isrepresented numerically, the method further comprising: obtaining SDOHfactor information for the patients associated with the patient medicalrecord information; receiving desired SDOH factor information;determining a first numeric demographic profile score for the patientsassociated with the patient medical record information based on the SDOHfactor information and the plurality of weights; determining a secondnumeric demographic profile score based on the desired SDOH factorinformation and the plurality of weights; and comparing the firstnumeric demographic profile score with the second numeric demographicprofile score to determine whether the demographic profile of patientsassociated with the patient medical record information meets the desireddemographic profile.
 6. The method of claim 1, wherein the firstscreening requirements define individual thresholds for one or more ofthe selection criteria and a matching threshold for a number of theindividual thresholds that must be met to match.
 7. The method of claim1, wherein the selection criteria comprise demographic information, oneor more diagnosis codes, laboratory test values, medication names,scores for cognitive tests, medical professional observations, acutecondition names, chronic condition names, and/or allergy names.
 8. Themethod of claim 1, wherein the plurality of providers is a firstplurality of providers, the method further comprising: receiving patientclaim information associated with a second plurality of providers, anumber of the second plurality of providers being greater than a numberof the first plurality of providers; and querying the patient claiminformation of each of the second plurality of providers usingdemographic information, one or more diagnosis codes, and/or pharmacyinformation to identify the first plurality of providers having patientswith third characteristics that match third screening requirements forthe clinical trial.
 9. The method of claim 8, further comprising:receiving a communication from each of the first plurality of providersopting in to participating in the clinical trial.
 10. The method ofclaim 8, further comprising: discarding ones of the first plurality ofproviders having a number of patients with the third characteristicsthat match the third screening requirements that does not exceed athreshold.
 11. The method of claim 8, further comprising: identifyingthe first plurality of providers based on geographic information for thefirst plurality of providers obtained from the patient claiminformation.
 12. The method of claim 1, wherein the second screeningrequirements comprise clinical characteristics associated with subjectmatter of the clinical trial; and wherein identifying, using theartificial intelligence engine, ones of the first subset of patientswhose medical record information includes second characteristics thatmatch second screening requirements for the clinical trial as clinicaltrial patient candidates comprises: determining, using a machinelearning engine or a multi-layer neural network, whether medical recordchart data for the first subset of patients matches any of the clinicalcharacteristics associated with the subject matter of the clinicaltrial; and determining, using a content similarity engine, whetherfree-text written by a health care practitioner contained in the medicalrecord chart data for the first subset of patients matches any of theclinical characteristics associated with the subject matter of theclinical trial.
 13. The method of claim 1, wherein the second screeningrequirements define individual thresholds for one or more of the secondcharacteristics and a matching threshold for a number of the individualthresholds that must be met to match.
 14. The method of claim 1, whereincommunicating identities of the clinical trial patient candidates toones of the plurality of providers that provide healthcare services tothe clinical trial patient candidates, respectively, comprises:providing access to the identities of ones of the clinical trial patientcandidates receiving healthcare services from one of the plurality ofproviders via a networked results portal accessible by the one of theplurality of providers.
 15. The method of claim 1, wherein communicatingidentities of the clinical trial patient candidates to ones of theplurality of providers that provide healthcare services to the clinicaltrial patient candidates, respectively, comprises: asynchronouslytransmitting a communication containing the identities of ones of theclinical trial patient candidates receiving healthcare services from oneof the plurality of providers to the one of the plurality of providers.16. A system, comprising: a processor; and a memory coupled to theprocessor and comprising computer readable program code embodied in thememory that is executable by the processor to perform operationscomprising: obtaining patient medical record information from aplurality of providers until a demographic profile of patientsassociated with the patient medical record information meets a desireddemographic profile; querying the patient medical record informationusing selection criteria to identify a first subset of patients havingfirst characteristics that match first screening requirements for aclinical trial; identifying, using an artificial intelligence engine,ones of the first subset of patients whose medical record informationincludes second characteristics that match second screening requirementsfor the clinical trial as clinical trial patient candidates; andcommunicating identities of the clinical trial patient candidates toones of the plurality of providers that provide healthcare services tothe clinical trial patient candidates, respectively; wherein theplurality of providers are associated with a plurality of differentorganizational managing entities, respectively.
 17. The system of claim16, wherein the demographic profile and the desired demographic profileare based on social determinants of health (SDOH) factors; and whereinthe SDOH factors comprise income, number of employers, educationallevel, transportation mode used, proximity to polluted air, proximity topolluted water, language competency, literacy status, access tonutritious foods, and/or access to physical activity opportunities. 18.The system of claim 17, wherein the desired demographic profilecomprises a plurality of weights corresponding to the SDOH factors,respectively, the plurality of weights representing a relativeimportance of the SDOH factors to each other.
 19. The system of claim18, wherein each of the SDOH factors is represented numerically, theoperations further comprising: obtaining SDOH factor information for thepatients associated with the patient medical record information;receiving desired SDOH factor information; determining a first numericdemographic profile score for the patients associated with the patientmedical record information based on the SDOH factor information and theplurality of weights; determining a second numeric demographic profilescore based on the desired SDOH factor information and the plurality ofweights; and comparing the first numeric demographic profile score withthe second numeric demographic profile score to determine whether thedemographic profile of patients associated with the patient medicalrecord information meets the desired demographic profile.
 20. A computerprogram product, comprising: a non-transitory computer readable storagemedium comprising computer readable program code embodied in the mediumthat is executable by a processor to perform operations comprising:obtaining patient medical record information from a plurality ofproviders until a demographic profile of patients associated with thepatient medical record information meets a desired demographic profile;querying the patient medical record information using selection criteriato identify a first subset of patients having first characteristics thatmatch first screening requirements for a clinical trial; identifying,using an artificial intelligence engine, ones of the first subset ofpatients whose medical record information includes secondcharacteristics that match second screening requirements for theclinical trial as clinical trial patient candidates; and communicatingidentities of the clinical trial patient candidates to ones of theplurality of providers that provide healthcare services to the clinicaltrial patient candidates, respectively; wherein the plurality ofproviders are associated with a plurality of different organizationalmanaging entities, respectively.